


'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerModel,
		)
		from transformers.models.roformer.modeling_tf_roformer import (
		    TFRoFormerSelfAttention,
		    TFRoFormerSinusoidalPositionalEmbedding,
		)




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=13   ,       lowerCamelCase_=7   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=99   ,       lowerCamelCase_=32   ,       lowerCamelCase_=2   ,       lowerCamelCase_=4   ,       lowerCamelCase_=37   ,       lowerCamelCase_="gelu"   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=5_12   ,       lowerCamelCase_=16   ,       lowerCamelCase_=2   ,       lowerCamelCase_=0.02   ,       lowerCamelCase_=3   ,       lowerCamelCase_=4   ,       lowerCamelCase_=None   ,       )			->				Tuple:
														lowerCAmelCase__			  =      parent
														lowerCAmelCase__			  =      13
														lowerCAmelCase__			  =      7
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      99
														lowerCAmelCase__			  =      32
														lowerCAmelCase__			  =      2
														lowerCAmelCase__			  =      4
														lowerCAmelCase__			  =      37
														lowerCAmelCase__			  =      '''gelu'''
														lowerCAmelCase__			  =      0.1
														lowerCAmelCase__			  =      0.1
														lowerCAmelCase__			  =      5_12
														lowerCAmelCase__			  =      16
														lowerCAmelCase__			  =      2
														lowerCAmelCase__			  =      0.02
														lowerCAmelCase__			  =      3
														lowerCAmelCase__			  =      4
														lowerCAmelCase__			  =      None







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size  )

														lowerCAmelCase__			  =      None
														if self.use_input_mask:
																					lowerCAmelCase__			  =      random_attention_mask([self.batch_size, self.seq_length]  )

														lowerCAmelCase__			  =      None
														if self.use_token_type_ids:
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.seq_length]   ,       self.type_vocab_size  )

														lowerCAmelCase__			  =      None
														lowerCAmelCase__			  =      None
														lowerCAmelCase__			  =      None
														if self.use_labels:
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size]   ,       self.type_sequence_label_size  )
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.seq_length]   ,       self.num_labels  )
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size]   ,       self.num_choices  )

														lowerCAmelCase__			  =      RoFormerConfig(
														    vocab_size=self.vocab_size   ,       hidden_size=self.hidden_size   ,       num_hidden_layers=self.num_hidden_layers   ,       num_attention_heads=self.num_attention_heads   ,       intermediate_size=self.intermediate_size   ,       hidden_act=self.hidden_act   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       max_position_embeddings=self.max_position_embeddings   ,       type_vocab_size=self.type_vocab_size   ,       initializer_range=self.initializer_range   ,       return_dict=lowerCamelCase_   ,       )

														return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[int]:
														lowerCAmelCase__			  =      TFRoFormerModel(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

														lowerCAmelCase__			  =      [input_ids, input_mask]
														lowerCAmelCase__			  =      model(lowerCamelCase_  )

														lowerCAmelCase__			  =      model(lowerCamelCase_  )

														self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[str]:
														lowerCAmelCase__			  =      True
														lowerCAmelCase__			  =      TFRoFormerForCausalLM(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}
														lowerCAmelCase__			  =      model(lowerCamelCase_  )['''logits''']
														self.parent.assertListEqual(
														    list(prediction_scores.numpy().shape  )   ,       [self.batch_size, self.seq_length, self.vocab_size]  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Dict:
														lowerCAmelCase__			  =      TFRoFormerForMaskedLM(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[Any]:
														lowerCAmelCase__			  =      self.num_labels
														lowerCAmelCase__			  =      TFRoFormerForSequenceClassification(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}

														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.num_choices
														lowerCAmelCase__			  =      TFRoFormerForMultipleChoice(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      tf.tile(tf.expand_dims(lowerCamelCase_   ,       1  )   ,       (1, self.num_choices, 1)  )
														lowerCAmelCase__			  =      tf.tile(tf.expand_dims(lowerCamelCase_   ,       1  )   ,       (1, self.num_choices, 1)  )
														lowerCAmelCase__			  =      tf.tile(tf.expand_dims(lowerCamelCase_   ,       1  )   ,       (1, self.num_choices, 1)  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': multiple_choice_inputs_ids,
														    '''attention_mask''': multiple_choice_input_mask,
														    '''token_type_ids''': multiple_choice_token_type_ids,
														}
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_choices)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				int:
														lowerCAmelCase__			  =      self.num_labels
														lowerCAmelCase__			  =      TFRoFormerForTokenClassification(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.num_labels)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[int]:
														lowerCAmelCase__			  =      TFRoFormerForQuestionAnswering(config=lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''input_ids''': input_ids,
														    '''attention_mask''': input_mask,
														    '''token_type_ids''': token_type_ids,
														}

														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.start_logits.shape   ,       (self.batch_size, self.seq_length)  )
														self.parent.assertEqual(result.end_logits.shape   ,       (self.batch_size, self.seq_length)  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.prepare_config_and_inputs()
														(
														    (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   (
														    lowerCAmelCase__
														)  ,   
														)			  =      config_and_inputs
														lowerCAmelCase__			  =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
														return config, inputs_dict




@require_tf
class 						a__							(				a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Optional[int]      		= (
							    (
							        TFRoFormerModel,
							        TFRoFormerForCausalLM,
							        TFRoFormerForMaskedLM,
							        TFRoFormerForQuestionAnswering,
							        TFRoFormerForSequenceClassification,
							        TFRoFormerForTokenClassification,
							        TFRoFormerForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							lowercase__     :  Tuple      		= (
							    {
							        "feature-extraction": TFRoFormerModel,
							        "fill-mask": TFRoFormerForMaskedLM,
							        "question-answering": TFRoFormerForQuestionAnswering,
							        "text-classification": TFRoFormerForSequenceClassification,
							        "text-generation": TFRoFormerForCausalLM,
							        "token-classification": TFRoFormerForTokenClassification,
							        "zero-shot": TFRoFormerForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)

							lowercase__     :  Optional[Any]      		= False
							lowercase__     :  List[Any]      		= False







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[str]:
														if pipeline_test_casse_name == "TextGenerationPipelineTests":
																					return True

														return False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      TFRoFormerModelTester(self  )
														lowerCAmelCase__			  =      ConfigTester(self   ,       config_class=lowerCamelCase_   ,       hidden_size=37  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														self.config_tester.run_common_tests()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_lm_head(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''  )
														self.assertIsNotNone(lowerCamelCase_  )




@require_tf
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''  )
														lowerCAmelCase__			  =      tf.constant([[0, 1, 2, 3, 4, 5]]  )
														lowerCAmelCase__			  =      model(lowerCamelCase_  )[0]

														# TODO Replace vocab size
														lowerCAmelCase__			  =      5_00_00

														lowerCAmelCase__			  =      [1, 6, vocab_size]
														self.assertEqual(output.shape   ,       lowerCamelCase_  )

														print(output[:, :3, :3]  )

														# TODO Replace values below with what was printed above.
														lowerCAmelCase__			  =      tf.constant(
														    [
														        [
														            [-0.12_053_341, -1.0_264_901, 0.29_221_946],
														            [-1.5_133_783, 0.197_433, 0.15_190_607],
														            [-5.0_135_403, -3.900_256, -0.84_038_764],
														        ]
														    ]  )
														tf.debugging.assert_near(output[:, :3, :3]   ,       lowerCamelCase_   ,       atol=1e-4  )




@require_tf
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  str      		= 1e-4







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      tf.constant([[4, 10]]  )
														lowerCAmelCase__			  =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=6   ,       embedding_dim=6  )

														lowerCAmelCase__			  =      emba(input_ids.shape  )
														lowerCAmelCase__			  =      tf.constant(
														    [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]  )

														tf.debugging.assert_near(lowerCamelCase_   ,       lowerCamelCase_   ,       atol=self.tolerance  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      tf.constant(
														    [
														        [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
														        [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
														        [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
														    ]  )
														lowerCAmelCase__			  =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12   ,       embedding_dim=5_12  )
														emba([2, 16, 5_12]  )
														lowerCAmelCase__			  =      emba.weight[:3, :5]

														tf.debugging.assert_near(lowerCamelCase_   ,       lowerCamelCase_   ,       atol=self.tolerance  )




@require_tf
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  int      		= 1e-4







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														# 2,12,16,64
														lowerCAmelCase__			  =      tf.reshape(tf.range(2 * 12 * 16 * 64   ,       dtype=tf.floataa  )   ,       shape=(2, 12, 16, 64)  ) / 1_00

														lowerCAmelCase__			  =      -tf.reshape(tf.range(2 * 12 * 16 * 64   ,       dtype=tf.floataa  )   ,       shape=(2, 12, 16, 64)  ) / 1_00

														lowerCAmelCase__			  =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=32   ,       embedding_dim=64  )
														lowerCAmelCase__			  =      embed_positions([2, 16, 7_68]  )[None, None, :, :]

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
														    lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      tf.constant(
														    [
														        [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
														        [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
														        [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
														        [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
														        [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
														        [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
														    ]  )
														lowerCAmelCase__			  =      tf.constant(
														    [
														        [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
														        [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
														        [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
														        [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
														        [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
														        [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
														    ]  )

														tf.debugging.assert_near(query_layer[0, 0, :6, :8]   ,       lowerCamelCase_   ,       atol=self.tolerance  )
														tf.debugging.assert_near(key_layer[0, 0, :6, :8]   ,       lowerCamelCase_   ,       atol=self.tolerance  )
import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


A						=			{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 1_3_1_0_7_2,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
}


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Union[str, Any]							)   ->    Optional[Any]:
		return torch.atana(UpperCamelCase			,       UpperCamelCase							) / math.pi * 2


def 	lowerCamelCase					(	UpperCamelCase     :				str							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  torch.sin(t * math.pi / 2							) ** 2
		_lowerCamelCase				   =  (1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(UpperCamelCase			,       UpperCamelCase							)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		pass





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     Any		)      ->	Optional[Any]:
				super().__init__()

				_lowerCamelCase				   =  DiffusionAttnUnetaD(snake_case__     ,  n_attn_layers=4		)
				_lowerCamelCase				   =  deepcopy(self.diffusion		)
				_lowerCamelCase				   =  torch.quasirandom.SobolEngine(1     ,  scramble=snake_case__		)



def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]							)   ->    List[str]:
		_lowerCamelCase				   =  MODELS_MAP[model_name]['url']
		os.system(F"""wget {url} ./"""							)

		return F"""./{model_name}.ckpt"""


A						=			{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
A						=			{
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
A						=			{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
    '8': 'resnets.3',
    '9': 'attentions.3',
    '10': 'resnets.4',
    '11': 'attentions.4',
    '12': 'resnets.5',
    '13': 'attentions.5',
}
A						=			{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

A						=			{
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

A						=			{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    int:
		if name.startswith('skip'							):
				return name.replace('skip'			,       RES_CONV_MAP['skip']							)

		# name has to be of format main.{digit}
		if not name.startswith('main.'							):
				raise ValueError(F"""ResConvBlock error with {name}"""							)

		return name.replace(name[:6]			,       RES_CONV_MAP[name[:6]]							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[Any]							)   ->    Tuple:
		for key, value in ATTN_MAP.items():
				if name.startswith(UpperCamelCase							) and not isinstance(UpperCamelCase			,       UpperCamelCase							):
						return name.replace(UpperCamelCase			,       UpperCamelCase							)
				elif name.startswith(UpperCamelCase							):
						return [name.replace(UpperCamelCase			,       UpperCamelCase							) for v in value]
		raise ValueError(F"""Attn error with {name}"""							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any			,       UpperCamelCase     :				int=13							)   ->    Optional[int]:
		_lowerCamelCase				   =  input_string

		if string.split('.'							)[0] == "timestep_embed":
				return string.replace('timestep_embed'			,       'time_proj'							)

		_lowerCamelCase				   =  0
		if string.startswith('net.3.'							):
				depth += 1
				_lowerCamelCase				   =  string[6:]
		elif string.startswith('net.'							):
				_lowerCamelCase				   =  string[4:]

		while string.startswith('main.7.'							):
				depth += 1
				_lowerCamelCase				   =  string[7:]

		if string.startswith('main.'							):
				_lowerCamelCase				   =  string[5:]

		# mid block
		if string[:2].isdigit():
				_lowerCamelCase				   =  string[:2]
				_lowerCamelCase				   =  string[2:]
		else:
				_lowerCamelCase				   =  string[0]
				_lowerCamelCase				   =  string[1:]

		if depth == max_depth:
				_lowerCamelCase				   =  MID_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  'mid_block'
		elif depth > 0 and int(UpperCamelCase							) < 7:
				_lowerCamelCase				   =  DOWN_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""down_blocks.{depth}"""
		elif depth > 0 and int(UpperCamelCase							) > 7:
				_lowerCamelCase				   =  UP_NUM_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""up_blocks.{max_depth - depth - 1}"""
		elif depth == 0:
				_lowerCamelCase				   =  DEPTH_0_TO_LAYER[layer_num]
				_lowerCamelCase				   =  F"""up_blocks.{max_depth - 1}""" if int(UpperCamelCase							) > 3 else 'down_blocks.0'

		if not string_left.startswith('.'							):
				raise ValueError(F"""Naming error with {input_string} and string_left: {string_left}."""							)

		_lowerCamelCase				   =  string_left[1:]

		if "resnets" in new_layer:
				_lowerCamelCase				   =  convert_resconv_naming(UpperCamelCase							)
		elif "attentions" in new_layer:
				_lowerCamelCase				   =  convert_attn_naming(UpperCamelCase							)
				_lowerCamelCase				   =  new_string_left

		if not isinstance(UpperCamelCase			,       UpperCamelCase							):
				_lowerCamelCase				   =  prefix + '.' + new_layer + '.' + string_left
		else:
				_lowerCamelCase				   =  [prefix + '.' + new_layer + '.' + s for s in string_left]
		return new_string


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]							)   ->    int:
		_lowerCamelCase				   =  {}
		for k, v in state_dict.items():
				if k.endswith('kernel'							):
						# up- and downsample layers, don't have trainable weights
						continue

				_lowerCamelCase				   =  rename(UpperCamelCase							)

				# check if we need to transform from Conv => Linear for attention
				if isinstance(UpperCamelCase			,       UpperCamelCase							):
						_lowerCamelCase				   =  transform_conv_attns(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
				else:
						_lowerCamelCase				   =  v

		return new_state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]			,       UpperCamelCase     :				Tuple			,       UpperCamelCase     :				Dict							)   ->    Optional[Any]:
		if len(UpperCamelCase							) == 1:
				if len(v.shape							) == 3:
						# weight
						_lowerCamelCase				   =  v[:, :, 0]
				else:
						# bias
						_lowerCamelCase				   =  v
		else:
				# qkv matrices
				_lowerCamelCase				   =  v.shape[0]
				_lowerCamelCase				   =  trippled_shape // 3
				for i in range(3							):
						if len(v.shape							) == 3:
								_lowerCamelCase				   =  v[i * single_shape : (i + 1) * single_shape, :, 0]
						else:
								_lowerCamelCase				   =  v[i * single_shape : (i + 1) * single_shape]
		return new_state_dict


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Optional[Any]:
		_lowerCamelCase				   =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

		_lowerCamelCase				   =  args.model_path.split('/'							)[-1].split('.'							)[0]
		if not os.path.isfile(args.model_path							):
				assert (
				    model_name == args.model_path
				), F"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
				_lowerCamelCase				   =  download(UpperCamelCase							)

		_lowerCamelCase				   =  MODELS_MAP[model_name]['sample_rate']
		_lowerCamelCase				   =  MODELS_MAP[model_name]['sample_size']

		_lowerCamelCase				   =  Object()
		_lowerCamelCase				   =  sample_size
		_lowerCamelCase				   =  sample_rate
		_lowerCamelCase				   =  0

		_lowerCamelCase				   =  UNetaDModel(sample_size=UpperCamelCase			,       sample_rate=UpperCamelCase							)
		_lowerCamelCase				   =  diffusers_model.state_dict()

		_lowerCamelCase				   =  DiffusionUncond(UpperCamelCase							)
		orig_model.load_state_dict(torch.load(args.model_path			,       map_location=UpperCamelCase							)['state_dict']							)
		_lowerCamelCase				   =  orig_model.diffusion_ema.eval()
		_lowerCamelCase				   =  orig_model.state_dict()
		_lowerCamelCase				   =  rename_orig_weights(UpperCamelCase							)

		_lowerCamelCase				   =  set(renamed_state_dict.keys()							) - set(diffusers_state_dict.keys()							)
		_lowerCamelCase				   =  set(diffusers_state_dict.keys()							) - set(renamed_state_dict.keys()							)

		assert len(UpperCamelCase							) == 0, F"""Problem with {renamed_minus_diffusers}"""
		assert all(k.endswith('kernel'							) for k in list(UpperCamelCase							)							), F"""Problem with {diffusers_minus_renamed}"""

		for key, value in renamed_state_dict.items():
				assert (
				    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
				), F"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
				if key == "time_proj.weight":
						_lowerCamelCase				   =  value.squeeze()

				_lowerCamelCase				   =  value

		diffusers_model.load_state_dict(UpperCamelCase							)

		_lowerCamelCase				   =  1_00
		_lowerCamelCase				   =  33

		_lowerCamelCase				   =  IPNDMScheduler(num_train_timesteps=UpperCamelCase							)

		_lowerCamelCase				   =  torch.manual_seed(UpperCamelCase							)
		_lowerCamelCase				   =  torch.randn([1, 2, config.sample_size]			,       generator=UpperCamelCase							).to(UpperCamelCase							)

		_lowerCamelCase				   =  torch.linspace(1			,       0			,       steps + 1			,       device=UpperCamelCase							)[:-1]
		_lowerCamelCase				   =  get_crash_schedule(UpperCamelCase							)

		_lowerCamelCase				   =  DanceDiffusionPipeline(unet=UpperCamelCase			,       scheduler=UpperCamelCase							)

		_lowerCamelCase				   =  torch.manual_seed(33							)
		_lowerCamelCase				   =  pipe(num_inference_steps=UpperCamelCase			,       generator=UpperCamelCase							).audios

		_lowerCamelCase				   =  sampling.iplms_sample(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       {}							)
		_lowerCamelCase				   =  generated.clamp(-1			,       1							)

		_lowerCamelCase				   =  (generated - audio).abs().sum()
		_lowerCamelCase				   =  (generated - audio).abs().max()

		if args.save:
				pipe.save_pretrained(args.checkpoint_path							)

		print('Diff sum'			,       UpperCamelCase							)
		print('Diff max'			,       UpperCamelCase							)

		assert diff_max < 1e-3, F"""Diff max: {diff_max} is too much :-/"""

		print(F"""Conversion for {model_name} successful!"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()

				parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
				parser.add_argument(
				    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
				)
				parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
				A						=			parser.parse_args()

				main(args)